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SPARK (Scientific Paper Abstracted Reasoning sKeleton), a paper-oriented synthesis framework built on Sci-Base, is introduced, a scientific reasoning dataset with substantially higher difficulty and diversity than existing resources and consistently outperforms existing scientific reasoning datasets.
This work presents a comprehensive framework for the development and evaluation of specialized scholarly agents, InternReviewer and InternAdvocate, by implementing an agentic Reinforcement Learning (RL) paradigm driven by a unified objective metric and reward system.
An RL method tailored for context management is proposed, which uses context and entropy variation to identify critical editing decisions for branch sampling and estimates action-level advantages from all branched trajectories that pass through the corresponding context editing action.
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